Spatial Distribution and Location Characteristics of Airbnb in Seoul, Korea

Impacts of COVID-19 on bike-sharing usages in Seoul, South Korea

The COVID-19 pandemic and social distancing restrictions have had a significant impact on urban mobility. As micro mobility offers less contact with other people, docked or dockless e-scooters and bike-sharing have emerged as alternative urban mobility solutions. However, little empirical research has been conducted to investigate how COVID-19 might affect micro mobility usage, especially in a major Asian city. This research aims to study how COVID-19 and other related factors have affected bike-sharing ridership in Seoul, South Korea. Using detailed urban telecommunication data, this study explored the spatial-temporal patterns of a docked bike-sharing system in Seoul. Stepwise negative binomial panel regressions were conducted to find out how COVID-19 and various built environments might affect bike-sharing ridership in the city. Our results showed that open space areas and green infrastructure had statistically significant positive impacts on bike-sharing usage. Compared to registered population factors, real-time telecommunication floating population had a significant positive relationship with both bike trip count and trip duration. The model showed that telecommunication floating population has a significant positive impact on bike-sharing trip counts and trip duration. These findings could offer useful guidelines for emerging shared mobility planning during and after the COVID-19 pandemic.

A global-scale analysis of the sharing economy model - an AirBnB case study

The sharing economy model has changed the way in which people engage in a variety of activities, including travelling, trading, working, and lending/borrowing money. Several studies exist that aim to understand, quantify and model such phenomenon, but most such studies are geographically focused on countries in the Western World. Knowledge about the penetration and adoption of this novel market model in non-Western countries is much more limited, and almost completely lacking when it comes to emerging markets, where it was touted to bring the biggest benefits and be a game changer to uplift people economically. To close the gap, we chose Airbnb as an example of sharing economy model with worldwide market penetration, and performed a large-scale quantitative study of its penetration and adoption in seven cities in Asia, five cities in Latin America. We compared findings against seven cities in the Western World, and observed patterns to be similar across all locales, with two notable exceptions: the geographic penetration of such services, and the experience that guests travelling to such destinations shared in their reviews.

Understanding the Implementation of Airbnb in Urban Contexts: Towards a Categorization of European Cities

The sharing economy has experienced exponential growth in recent years, especially in the short-term rentals (STRs) tourist accommodation sector. This growth has caused disruptive effects in rural and urban contexts, especially in highly touristic cities. These effects can be both positive and negative, revitalizing certain areas and bringing about tension in the socioeconomic fabric. Today, Airbnb is considered the paradigm of this sharing economy model and the STR industry leader. However, as this study suggests, on many occasions the implementation of Airbnb exhibits more of a traditional economic business model than a collaborative economic business model. Through hierarchical cluster analysis, this study identifies different groups of European cities according to the degree of professionalization of Airbnb implementation in their territory. The goal is to find similar patterns in the Airbnbisation process in major European cities, as the social, economic, and spatial impacts of various typologies are very different and even contrary. By understanding and identifying such different models implemented in each territory, better policies can be informed, and more adapted strategies can be pursued by local governments and the tourism industry.

Innovative Tools for Tourism and Cultural Tourism Impact Assessment

The importance of data and evidence has increased considerably in policy planning, implementation, and evaluation. There is unprecedented availability of open and big data, and there are rapid developments in intelligence gathering and the application of analytical tools. While cultural heritage holds many tangible and intangible values for local communities and society in general, there is a knowledge gap regarding suitable methods and data sources to measure the impacts and develop data-driven policies of cultural tourism. In the tourism sector, rapid developments are particularly taking place around novel uses of mobile positioning data, web scraping, and open application programming interface (API) data, data on sharing, and collaborative economy and passenger data. Based on feedback from 15 European cultural tourism regions, recommendations are developed regarding the use of innovative tools and data sources in tourism management. In terms of potential analytical depth, it is especially advisable to explore the use of mobile positioning data. Yet, there are considerable barriers, especially in terms of privacy protection and ethics, in using such data. User-generated big data from social media, web searches, and website visits constitute another promising data source as it is often publicly available in real time and has low usage barriers. Due to the emergence of new platform-based business models in the travel and tourism sector, special attention should be paid to improving access and usage of data on sharing and collaborative economy.